Literature DB >> 15879511

The coupon collector and the suppressor mutation: estimating the number of compensatory mutations by maximum likelihood.

Art Poon1, Bradley H Davis, Lin Chao.   

Abstract

Compensatory mutation occurs when a loss of fitness caused by a deleterious mutation is restored by its epistatic interaction with a second mutation at a different site in the genome. How many different compensatory mutations can act on a given deleterious mutation? Although this quantity is fundamentally important to understanding the evolutionary consequence of mutation and the genetic complexity of adaptation, it remains poorly understood. To determine the shape of the statistical distribution for the number of compensatory mutations per deleterious mutation, we have performed a maximum-likelihood analysis of experimental data collected from the suppressor mutation literature. Suppressor mutations are used widely to assess protein interactions and are under certain conditions equivalent to compensatory mutations. By comparing the maximum likelihood of a variety of candidate distribution functions, we established that an L-shaped gamma distribution (alpha=0.564, theta=21.01) is the most successful at explaining the collected data. This distribution predicts an average of 11.8 compensatory mutations per deleterious mutation. Furthermore, the success of the L-shaped gamma distribution is robust to variation in mutation rates among sites. We have detected significant differences among viral, prokaryotic, and eukaryotic data subsets in the number of compensatory mutations and also in the proportion of compensatory mutations that are intragenic. This is the first attempt to characterize the overall diversity of compensatory mutations, identifying a consistent and accurate prior distribution of compensatory mutation diversity for theoretical evolutionary models.

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Year:  2005        PMID: 15879511      PMCID: PMC1451182          DOI: 10.1534/genetics.104.037259

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  23 in total

Review 1.  The biological cost of antibiotic resistance.

Authors:  D I Andersson; B R Levin
Journal:  Curr Opin Microbiol       Date:  1999-10       Impact factor: 7.934

2.  Pervasive compensatory adaptation in Escherichia coli.

Authors:  F B Moore; D E Rozen; R E Lenski
Journal:  Proc Biol Sci       Date:  2000-03-07       Impact factor: 5.349

3.  Rapid fitness recovery in mutationally degraded lines of Caenorhabditis elegans.

Authors:  Suzanne Estes; Michael Lynch
Journal:  Evolution       Date:  2003-05       Impact factor: 3.694

4.  Evolution by small steps and rugged landscapes in the RNA virus phi6.

Authors:  C L Burch; L Chao
Journal:  Genetics       Date:  1999-03       Impact factor: 4.562

5.  Maximum-Likelihood Models for Combined Analyses of Multiple Sequence Data

Authors: 
Journal:  J Mol Evol       Date:  1996-05       Impact factor: 2.395

Review 6.  Mechanisms of suppression.

Authors:  P E Hartman; J R Roth
Journal:  Adv Genet       Date:  1973       Impact factor: 1.944

7.  RNA folding in Drosophila shows a distance effect for compensatory fitness interactions.

Authors:  W Stephan; D A Kirby
Journal:  Genetics       Date:  1993-09       Impact factor: 4.562

8.  Reducing antibiotic resistance.

Authors:  S J Schrag; V Perrot
Journal:  Nature       Date:  1996-05-09       Impact factor: 49.962

9.  Synonymous nucleotide divergence and saturation: effects of site-specific variations in codon bias and mutation rates.

Authors:  O G Berg
Journal:  J Mol Evol       Date:  1999-04       Impact factor: 2.395

10.  Compensatory mutations cause excess of antagonistic epistasis in RNA secondary structure folding.

Authors:  Claus O Wilke; Richard E Lenski; Christoph Adami
Journal:  BMC Evol Biol       Date:  2003-02-05       Impact factor: 3.260

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  42 in total

1.  The nearly neutral and selection theories of molecular evolution under the fisher geometrical framework: substitution rate, population size, and complexity.

Authors:  Pablo Razeto-Barry; Javier Díaz; Rodrigo A Vásquez
Journal:  Genetics       Date:  2012-03-16       Impact factor: 4.562

2.  Dynamic mutation-selection balance as an evolutionary attractor.

Authors:  Sidhartha Goyal; Daniel J Balick; Elizabeth R Jerison; Richard A Neher; Boris I Shraiman; Michael M Desai
Journal:  Genetics       Date:  2012-06-01       Impact factor: 4.562

Review 3.  The population genetics of antibiotic resistance: integrating molecular mechanisms and treatment contexts.

Authors:  R Craig MacLean; Alex R Hall; Gabriel G Perron; Angus Buckling
Journal:  Nat Rev Genet       Date:  2010-06       Impact factor: 53.242

4.  Lack of evidence for sign epistasis between beneficial mutations in an RNA bacteriophage.

Authors:  Andrea J Betancourt
Journal:  J Mol Evol       Date:  2010-10-12       Impact factor: 2.395

Review 5.  Experimental approaches to evaluate the contributions of candidate protein-coding mutations to phenotypic evolution.

Authors:  Jay F Storz; Anthony J Zera
Journal:  Methods Mol Biol       Date:  2011

6.  Selective sweeps and parallel mutation in the adaptive recovery from deleterious mutation in Caenorhabditis elegans.

Authors:  Dee R Denver; Dana K Howe; Larry J Wilhelm; Catherine A Palmer; Jennifer L Anderson; Kevin C Stein; Patrick C Phillips; Suzanne Estes
Journal:  Genome Res       Date:  2010-10-29       Impact factor: 9.043

7.  The rate of compensatory mutation in the DNA bacteriophage phiX174.

Authors:  Art Poon; Lin Chao
Journal:  Genetics       Date:  2005-05-23       Impact factor: 4.562

Review 8.  Genetic constraints on protein evolution.

Authors:  Manel Camps; Asael Herman; Ern Loh; Lawrence A Loeb
Journal:  Crit Rev Biochem Mol Biol       Date:  2007 Sep-Oct       Impact factor: 8.250

9.  Compensatory mutations are repeatable and clustered within proteins.

Authors:  Brad H Davis; Art F Y Poon; Michael C Whitlock
Journal:  Proc Biol Sci       Date:  2009-02-25       Impact factor: 5.349

10.  Limits to Compensatory Mutations: Insights from Temperature-Sensitive Alleles.

Authors:  Katarzyna Tomala; Piotr Zrebiec; Daniel L Hartl
Journal:  Mol Biol Evol       Date:  2019-09-01       Impact factor: 16.240

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